Efficient coding of visual information in neural computations

Laurent Perrinet

LACONEU 2019: 5th Latin-American Summer School in Computational Neuroscience, 16/1/2019

(AUTHOR) Hello, I am Laurent Perrinet from the Institute of Neurosciences of la Timone in Marseille, a joint unit from the CNRS and the please interrupt
(OBJECTIVE) in this talk, I will be focus in highlighting some key challenges in understanding visual perception in terams of efficient coding using modelization and neural data and
(ACKNO) this endeavour involves different techniques and tools ... From the head on, I wish to thanks people who collaborated and in particular .. mostly funded by the ANR horizontal V1 (fregnac chavane) + ANR TRAJECTORY (o marrre bruno cessac palacios )
LONDON (Jim Bednar, Friston)
(SHOW TITLE) I am interested in the link between the neural code and the structure of the world. in particular, for vision, I am researching the relation between the functional (in terms of the inferential processes leading to behaviour) organization (anatomy and activity) of low-level visual areas (V1) and the structures of natural scenes, that is of the images that hit the retina and which are relevant to visual perception in general.

so what is visual perception?

LP (2015) "Sparse models" in Biologically Inspired Computer Vision

Ravello, Escobar, Palacios, LP (2019) in prep

Bosking et al. (1997) Journal of Neuroscience

... is the set of long-range lateral connections between neurons, which could act to facilitate detection of contours matching the association field, and/or inhibit detection of other contours. To fill this role, the lateral connections would need to be orientation specific and aligned along contours, * (colin) and indeed such an arrangement has been found in treeshrew's primary visual cortex (Bosking et al., J Neurosci 17:2112-27, 1997) (neural) if one looks at the primary visual area in the occipital lobe of the cortex using optical imaging as here in the treeshrew by Bosking and colleagues under the supervision of DF, one could represent the distributed, topographical representation of orientation selectivity. in (A) and (B) the orientation giving the most response at each cortical position is represented by hue using the code below from orange for horizontal to blue for verticals, and typical structures are magnified in (C): stripes (on the periphery) and pinwheels. You can understand this as a packing of a 3D feature space on the 2D surface of the cortex. (method) Tree shrew orientation preference maps were obtained using optical imaging. Additionally, 540 nm light was used to map surface blood vessels used for alignment. Biocytin was then injected into a specific site in V1 and the animal was sacrificed 16 hours later. Slices of V1 were imaged to locate the biocytin bouton and the surface blood vessels. The blood vessel information was then used to align the orientation preference maps with the bouton images giving overlaid information on the underlying connectivity from the injection site on the animal. The original experiment used a total of ten cases. (lateral) we show here one result of Bosking which overlay over a map or orientation selectivity the network of lateral connectivity originating froma group of neurons with similar orientations and position. There is a structure in this connectivity towards locality (more pronounced for site B) + connecting iso orientations even on long ranges (A). This type of structure tends to wire together those neurons that have similar orientations, indicating a prior to colinearities. (colin) ... Overall, a typical assumption that the role of lateral interactions is to enhance the activity of neurons which are collinear : it is the so-called association field formalized in Field 93, as was for instance modeled neurally in the work from P. Series or in this version for computer vision (physio) is there a match of these structures with the statistics of natural images? 2) Some authors (Kisvarday, 1997, Chavane and Monier) even say it is weak or inexistent on a the scale of the area... 1: Hunt & Goodhill have reinterpreted above data and shown that there is more diversity than that - TRANSITION : my goal here will be to tackle this problem at different levels:

Outline

Efficiency, vision and neurons

Sparse coding

Sparse Hebbian Learning

LP and Bednar (2015) Scientific Reports, http://www.nature.com/articles/srep11400

We first extracted the edges from images using a scale-space analysis (all OSS on github) The histogram was computed as a a 4-dimensional function of distance, (symmetrical) azimuth $\psi$, difference of orientation $ heta$ and ratio of scale. ... second-order statistics are efficiently computed by using a the algorithm from Geisler et al. (2001), with a more general edge extraction algorithm that uses sparse coding %to avoid multiple responses to a single edge. * ... Collinearity and co-circularity results for natural images replicated qualitatively the results from Geisler et al. (2001), confirming that prior information about continuations appeared consistently in natural images.

Probability distribution function of "chevrons" * (angles) By computing measures of the independence of the different variables, we found that the probability density function of the second-order statistics of edges factorizes with on one side distance and scale and on the other side the 2 angles. The first component proved to be quite similar across both classes and the greater difference is seen for different angle configuration. As it can be reduced to 2 dimensions, we can plot the full probability as shown here by different contrast values assigned to all possible chevrons configurations, for all possible "azimuth" values $\psi$ on the horizontal axis and difference of orientation $ heta$ on the vertical axis. Such a plot most strikingly shows the difference between these 2 classes. one issue now that we can show the 2nd order statistics is to know if it would be possible to quantify such difference...

(colin) let's first replicate the result from Geisler by showing that relative to a given edge (segment in the center), what is the Here I show for each distance and angle the most probable difference of angle, showing that collinear and parallel edges predominate. (cocir) a similar pattern is observed the cocircular plot. it reproduces the results from Geisler on natural images, but laboratory environment shows a strong bias to colinearity. If we believe bosking link, obviously, this should have a consequence on antomy. thus we test whether these AF were different across different image classes.